Genes regulating levels of ω‐3 long‐chain polyunsaturated fatty acids are associated with alcohol use disorder and consumption, and broader externalizing behavior in humans

Abstract Background Individual variation in the physiological response to alcohol is predictive of an individual's likelihood to develop alcohol use disorder (AUD). Evidence from diverse model organisms indicates that the levels of long‐chain polyunsaturated omega‐3 fatty acids (ω‐3 LC‐PUFAs) can modulate the behavioral response to ethanol and therefore may impact the propensity to develop AUD. While most ω‐3 LC‐PUFAs come from diet, humans can produce these fatty acids from shorter chain precursors through a series of enzymatic steps. Natural variation in the genes encoding these enzymes has been shown to affect ω‐3 LC‐PUFA levels. We hypothesized that variation in these genes could contribute to the susceptibility to develop AUD. Methods We identified nine genes (FADS1, FADS2, FADS3, ELOVL2, GCKR, ELOVL1, ACOX1, APOE, and PPARA) that are required to generate ω‐3 LC‐PUFAs and/or have been shown or predicted to affect ω‐3 LC‐PUFA levels. Using both set‐based and gene‐based analyses we examined their association with AUD and two AUD‐related phenotypes, alcohol consumption, and an externalizing phenotype. Results We found that the set of nine genes is associated with all three phenotypes. When examined individually, GCKR, FADS2, and ACOX1 showed significant association signals with alcohol consumption. GCKR was significantly associated with AUD. ELOVL1 and APOE were associated with externalizing. Conclusions Taken together with observations that dietary ω‐3 LC‐PUFAs can affect ethanol‐related phenotypes, this work suggests that these fatty acids provide a link between the environmental and genetic influences on the risk of developing AUD.


INTRODUC TI ON
The misuse of alcohol is a major social and healthcare problem.
Alcohol use is linked to significant negative health outcomes; the World Health Organization estimates that alcohol consumption is the cause of 5% of the global disease burden (World Health Organization (Geneva), 2018), and alcohol use contributes to 3 million deaths per year worldwide (World Health Organization (Geneva), 2018). The risk underlying the development of alcohol use disorder (AUD) has both genetic and non-genetic components; each is responsible for approximately half of the lifetime liability to develop AUD (Prescott & Kendler, 1999). We are interested in the interface of genetics and environment in AUD liability. We recently demonstrated that an environmental factor, dietary long-chain polyunsaturated omega-3 fatty acids (ω-3 LC-PUFAs), plays an important role in acute behavioral responses to alcohol in invertebrates and mammals (Raabe et al., 2014;Wolstenholme et al., 2018). We found that ω-3 LC-PUFAs in the diet interact with the genetic background in mice to modulate voluntary consumption of alcohol (Wolstenholme et al., 2018). In human adolescents, serum ω-3 LC-PUFAs levels were associated with alcohol sensitivity (Edwards et al., 2019). Together, these studies support the importance of ω-3 LC-PUFAs in alcohol phenotypes.
While diet is the major influencer of ω-3 PUFA levels in humans, approximately a quarter of the variation in ω-3 PUFA levels can be explained by genetics (Harris et al., 2012). There are common genetic variants of genes encoding proteins involved in converting ALA to the ω-3 LC-PUFAs, and some of these can significantly influence the tissue and serum levels of ω-3 LC-PUFAs (Al-Hilal et al., 2013;Alsaleh et al., 2014;Baylin et al., 2007;Gillingham et al., 2013;Huang et al., 2014;Lemaitre et al., 2011;Mathias et al., 2010;Plourde et al., 2009;Schaeffer et al., 2006;Tanaka et al., 2009). ALA is converted to EPA through actions of the Δ6 desaturase FADS2, the elongase ELOVL1, and the Δ5 desaturase FADS1. EPA is converted into DPA by the elongase ELOVL2, and DPA is converted to DHA using the acyl-CoA oxidase ACOX1. Functional allelic variation in these genes would be predicted to influence ω-3 LC-PUFA levels, and indeed, a number of studies have identified allelic variation in FADS1, FADS2, ELOVL2, and four additional genes, FADS3, GCKR, APOE, and PPARA, as being associated with significant differences in ω-3 LC-PUFA levels in humans (Al-Hilal et al., 2013;Alsaleh et al., 2014;Baylin et al., 2007;Gillingham et al., 2013;Huang et al., 2014;Lemaitre et al., 2011;Mathias et al., 2010;Plourde et al., 2009;Schaeffer et al., 2006;Tai et al., 2005;Tanaka et al., 2009). To date, the contributions of genes regulating ω-3 LC-PUFA levels have not been directly tested for association with alcohol phenotypes in humans. We therefore examined these nine genes for association with three key alcohol-related outcomes available in large samples: alcohol consumption, alcohol use disorder, and externalizing (a composite phenotype consisting of disorders and behaviors characterized by behavioral under control, including alcohol problems). We hypothesized that variation in some or all of these genes affecting ω-3 LC-PUFA levels would be associated with one or more of these AUD-related phenotypes.

MATERIAL S AND ME THODS
We analyzed genotypic data for the genes of interest from genomewide association data available on our three alcohol-related phenotypes: alcohol consumption, alcohol use disorder, and externalizing.
A full list of the GWAS and their composite samples are presented in Table 1.

Alcohol consumption
We generated GWAS by meta-analyzing two publicly available, large-scale GWAS of alcohol consumption. The first dataset comes from the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN) analysis of drinks per week (without the 23&me subsample) in approximately 550 K individuals of European ancestries (Liu et al., 2019). The second GWAS comes from the Million Veterans Program (MVP) GWAS of the first three items related to alcohol consumption in the Alcohol Use Disorder Identification Test (AUDIT), referred to as AUDIT-C, in approximately 200 K individuals of European ancestries (Kranzler et al., 2019). These sets of summary statistics were highly correlated (r g = 0.70), and we used a sample size-weighted meta-analysis in METAL (Willer et al., 2010), resulting in the total sample size of ~750 K individuals.

Alcohol use disorder (AUD)
We created GWAS data for AUD by meta-analyzing three sets of summary statistics: AUD as assessed using DSM-IV Alcohol dependence criteria measured in the Million Veterans Program (N ~ 200 K) (Kranzler et al., 2019;Zhou et al., 2020), DSM-IV Alcohol Dependence criteria as analyzed by the Psychiatric Genomics Consortium (N ~ 29 K) (Walters et al., 2018), and the problem subscale of the AUDIT analyzed in the UK Biobank (AUDIT-P, N ~ 121 K) (Sanchez-Roige et al., 2019), again using a sample size-weighted meta-analysis because of the relatively strong genetic overlap between each (r g = 0.60 to 1.00). UK Biobank AUDIT-P is based on items 4 to 10 of the AUDIT, which is summed to produce an overall problem scale score. This score was log10 transformed in analyses to approximate a normal distribution. Our final sample size was N ~ 350 K individuals of European ancestries.

Externalizing
Externalizing is an umbrella term used to describe a variety of behaviors/problems related to behavioral disinhibition that are correlated at the phenotypic and genetic levels (Barr & Dick, 2020). We Our primary analyses were gene-set analyses that tested for overall association with the set of nine genes (FADS1, FADS2, FADS3, ELOVL2, GCKR, ELOVL1, ACOX1, APOE, PPARA) and the three alcohol-related outcomes. We created a gene set containing all nine genes and ran gene-set analysis using the R COMBAT package (Wang et al., 2017). COMBAT requires only SNP level p-values and correlations between SNPs from ancestry-matched samples and performs an extended Simes procedure to combine multiple parallel association test results performed by using Gates, Vegas (five tests and combined test), and simpleM methods. COMBAT then creates an overall association p-value.
Subsequently, we performed gene-based tests with each of the individual genes. We first performed MAGMA gene-based analysis and gene-set analysis on the full GWAS input data through FUMA using the alcohol consumption, AUD, and externalizing GWAS results (Watanabe et al., 2017). FUMA uses input GWAS summary statistics to compute gene-based p-values (gene analysis). The gene-based p-value for gene analysis is computed for protein-coding genes by mapping SNPs to genes if SNPs are located within the genes. GWAS results from each of the samples were loaded to FUMA SNP2GENE software for gene-based analyses.
For robust comparison of FUMA results, we ran FastBAT which performs a set-based association analysis for human complex traits using summary-level data from GWAS and linkage disequilibrium (LD) data from a reference sample with individual-level genotypes.
For the reference panel, we used 1000 Genome data. These data agree with the FUMA analysis and appear in Table S1. Table 2 shows the results from all of the COMBAT analyses. The ninegene set (FADS1, FADS2, FADS3, ELOVL2, GCKR, ELOVL1, ACOX1, APOE, PPARA) was highly associated with all three phenotypes. showed suggestive signals of association (p < 0.1) with alcohol consumption. GCKR was significantly associated with AUD. ELOVL1 was strongly associated with externalizing. APOE also was significantly associated with externalizing (p = 0.03).
Humans can also make ω-3 PUFAs; the short chain ω-3 PUFA, αlinolenic acid (ALA), derived from plant sources, can be converted to EPA through a series of elongation and desaturation steps. EPA can be converted to DPA and then to DHA. The conversion of ALA to EPA is inefficient relative to acquiring EPA and DHA from dietary sources, and this conversion is thought to be important in regulating ω-3 PUFA levels in people who do not eat seafood (Burdge & Wootton, 2003;Goyens et al., 2005Goyens et al., , 2006.
Here we tested for association between genes involved in the generation of ω-3 LC-PUFAs and genes with allelic variants that modify ω-3 LC-PUFA levels, and alcohol-related phenotypes in humans.
We identified nine genes that are directly required for the generation of ω-3 LC-PUFAs (FADS1, FADS2, ELOVL1, ELOVL2, and ACOX1) or for which we found evidence from human studies that they are When tested as a set, we found that the nine genes were significantly associated with all alcohol-related phenotypes examined: alcohol consumption, AUD, and externalizing. We also found that several of these genes were individually significantly associated with different alcohol-related phenotypes. We found that variation in GCKR, FADS2, and ACOX1 was significantly associated with the consumption phenotype, GCKR was associated with AUD, and ELOVL1 and APOE were associated with externalizing.
We have previously shown that dietary levels of ω-3 LC-PUFAs Abbreviations: NSNPS, the number of SNPs from the corresponding GWAS annotated to that gene that was found in the data and was not excluded based on internal SNP QC; p, the gene p-value, using asymptotic sampling distribution; ZSTAT, the Z-value for the gene, based on its (permutation) p-value. required for the development of acute functional tolerance to EtOH, one component of LR, in the invertebrate model C. elegans, and that increasing the levels of EPA enhanced acute functional tolerance (Raabe et al., 2014). We extended these observations to mice and found that dietary supplementation of the ω-3 LC-PUFAs EPA and DHA in the form of fish oil also modulated both the acute stimulatory and sedative effects of EtOH, as tested by locomotor activation and loss of righting reflex assays (Wolstenholme et al., 2018). These di-  (Gawrisch & Soubias, 2008;Shaikh et al., 2015). The levels of ω-3 LC-PUFAs have significant effects on membrane microarchitecture, which in turn can regulate the activity of membrane-bound proteins (Fan et al., 2003). Membrane microarchitecture can also change how and if EtOH interacts with its direct targets. For example, the ability of EtOH to interact with its direct target, the SLO-1 BK potassium channel, is significantly altered by the composition of the membrane in which the channel resides (Yuan et al., 2008). The GABA A receptor is another major direct target of EtOH's actions, and membrane microarchitecture can affect drug binding to GABA A receptors (Nothdurfter et al., 2013). Taken together, these data suggest a model in which ω-3 LC-PUFAs levels may modulate both neuronal functions and directly influence the effects of EtOH on the brain.
LC-PUFAs are also the backbones of several types of lipid signaling molecules, including the eicosanoids, which are responsible for both proinflammatory and anti-inflammatory actions. The ω-3 LC-PUFAs EPA and DHA are the bases of the resolvins, a group of eicosanoids that are involved in the resolution of inflammation, including in the brain (Labrousse et al., 2018;Madore et al., 2020), and decreases in resolvin biosynthesis have been observed in several brain diseases (recently comprehensively reviewed in Dyall et al., 2022). Pro-inflammatory eicosanoids are generated from the ω-6 LC-PUFAs, so it is thought that the ratio of ω-3/ ω-6 LC-PUFAs is important in determining the levels of inflammation. Heavy alcohol drinking causes neuroinflammation (Crews et al., 2013;King et al., 2020;Pascual et al., 2014), suggesting that ω-3 LC-PUFA mediated effects on inflammation may therefore be important in modulating these physiological effects of EtOH.
Human genetic studies have also provided clues for the possible roles of ω-3 LC-PUFAs in alcohol phenotypes. One of the bestsupported candidate loci for association with human AUD phenotypes is the gene KLB (Jorgenson et al., 2017;Sanchez-Roige et al., 2019;Schumann et al., 2016). Animal studies implicate ω-3 LC-PUFAs in the function of KLB: Fish oil supplementation significantly increased KLB expression (both mRNA and protein) in the liver in mice (Yang et al., 2017). KLB is required for the function of FGF21, which functions in the brain to regulate alcohol preference in mice (Schumann et al., 2016), and acts as an important lipid metabolism regulator. Fish oil supplementation also increased the signaling of FGF21 in mice (Yang et al., 2017). In humans, an explicit relationship between dietary EPA and FGF21 levels was recently described; EPA supplementation increased circulating FGF21 levels (Escote et al., 2018).
Among the genes that we tested, GCKR has previously been shown to be associated with alcohol consumption in the UK Biobank (Clarke et al., 2017), with drinks per week in GSCAN (Liu et al., 2019), and with total AUDIT score in a composite sample of UK Biobank and 23andMe (Sanchez-Roige et al., 2019). Intriguingly, allelic variation in GCKR had been previously been found to be associated in a GWAS from the CHARGE consortium with variation in levels of the ω-3 LC-PUFA DPA, an intermediate product of the conversion of EPA to DHA (Lemaitre et al., 2011). However, it is important to note that these previous analyses were based on samples that were also incorporated into the larger meta-analytic results examined in the current analysis and thus should not be interpreted as independent evidence.
Our findings should be interpreted in the context of the following limitations. Because genetic analyses require large sample sizes for adequate power and the reliable and replicable detection of effects (Hong & Park, 2012), we limited our analyses to large extant samples with genotypic data. This necessarily limits the depth of phenotyping available. Follow-up analyses should further characterize the pathways by which genetic and environmental factors jointly contribute to ω-3 LC-PUFA levels to influence alcohol consumption and related behavioral regulation outcomes in humans. It is also likely that there are additional genes involved in the regulation of ω-3 LC-PUFA levels that were not examined here. We confined our analyses to genes for which there is extremely strong evidence of effects on ω-3 LC-PUFA levels. We tested genes encoding enzymes in the biosynthetic pathways known to directly generate the three main ω-3 LC-PUFAs or that have been shown to affect levels of ω-3 LC-PUFAs in humans, but it is very likely that other genes that are not represented in these two groups also regulate ω-3 LC-PUFA levels. We predict that such genes may also affect alcohol-related phenotypes.
Ultimately, our hope in characterizing the pathways that underlie risk

AUTH O R CO NTR I B UTI O N S
FA and PBB performed the analysis and interpreted findings. AGD, DMD, and JCB were responsible for the study concept, design, and interpretation of findings. All authors contributed to the drafting of the manuscript, and all authors critically reviewed the content and approved the final version for publication.

ACK N OWLED G M ENTS
These analyses were made possible by the generous public sharing of summary statistics from published GWAS from the Psychiatric

CO N FLI C T O F I NTE R E S T
The authors have no conflicts of interest to declare.